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Cut off Point of Insulin‑Like Growth Factor‑I (IGF‑1) for Prediction of Child Stunting


Affiliations
1 Department of Nutrition, Faculty of Medicine, Universitas Andalas Padang, Indonesia
2 Director of Community Nutrition, Ministry of Health, Republic of Indonesia, Indonesia
3 Department of Public Health and Community Medicine, Faculty of Medicine, Universitas Andalas, Padang City, Indonesia
4 Biomedical Laboratory, Faculty of Medicine, Universitas Andalas Padang,, Indonesia
5 Faculty of Public Health, Universitas Andalas Padang, Indonesia
     

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Objectives: The aim of this study was to determine cut off point of Insulin‑Like Growth Factor‑I (IGF‑1) for prediction of child stunting. Method: This prognostic model was conducted subdistrict of Pasaman and West Pasaman, West Sumatera, Indonesia from July‑November 2018. This study was performed on 185 children aged 0‑3 years, consist of stunting group 94 respondents and not stunting 91 respondents. Determination of insulin‑like growth factor‑I (IGF‑I) expression levels using the qRT‑PCR method. Total RNA from blood samples of stunting and normal children was extracted using Trizol and stunting assesment using z‑score index Height per age where the result ≤‑2 SD is stunting. The mean difference of IGF‑I level was analyzed by independent sample T test. A two‑tailed P‑value of <0.05 was considered statistically significant. Cut off point analysis using receiver operating characteristic (ROC) curve, the results show sensitivity, specificity and an accuracy. Data were analyzed using the SPSS version 20.0 Results: The results showed IGF level in child stunting 10.44±9.88 ng/ml and 10.09±10.08 ng/ml in child not stunting. Cut off point IGF‑I for prediction of child stunting is 6.63 ng/ml with 64.2% sensitivity, 60.0% specificity and accuracy 61,3%. Conclusion: This analysis confirmed IGF‑I can predict child stunting with enough accuracy for classification.

Keywords

Child, classification, IGF‑1, prediction, stunting
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  • Cut off Point of Insulin‑Like Growth Factor‑I (IGF‑1) for Prediction of Child Stunting

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Authors

Masrul
Department of Nutrition, Faculty of Medicine, Universitas Andalas Padang, Indonesia
Doddy Izwardy
Director of Community Nutrition, Ministry of Health, Republic of Indonesia, Indonesia
Ricvan Dana Nindrea
Department of Public Health and Community Medicine, Faculty of Medicine, Universitas Andalas, Padang City, Indonesia
Ikhwan Resmala Sudji
Biomedical Laboratory, Faculty of Medicine, Universitas Andalas Padang,, Indonesia
Idral Purnakarya
Faculty of Public Health, Universitas Andalas Padang, Indonesia

Abstract


Objectives: The aim of this study was to determine cut off point of Insulin‑Like Growth Factor‑I (IGF‑1) for prediction of child stunting. Method: This prognostic model was conducted subdistrict of Pasaman and West Pasaman, West Sumatera, Indonesia from July‑November 2018. This study was performed on 185 children aged 0‑3 years, consist of stunting group 94 respondents and not stunting 91 respondents. Determination of insulin‑like growth factor‑I (IGF‑I) expression levels using the qRT‑PCR method. Total RNA from blood samples of stunting and normal children was extracted using Trizol and stunting assesment using z‑score index Height per age where the result ≤‑2 SD is stunting. The mean difference of IGF‑I level was analyzed by independent sample T test. A two‑tailed P‑value of <0.05 was considered statistically significant. Cut off point analysis using receiver operating characteristic (ROC) curve, the results show sensitivity, specificity and an accuracy. Data were analyzed using the SPSS version 20.0 Results: The results showed IGF level in child stunting 10.44±9.88 ng/ml and 10.09±10.08 ng/ml in child not stunting. Cut off point IGF‑I for prediction of child stunting is 6.63 ng/ml with 64.2% sensitivity, 60.0% specificity and accuracy 61,3%. Conclusion: This analysis confirmed IGF‑I can predict child stunting with enough accuracy for classification.

Keywords


Child, classification, IGF‑1, prediction, stunting



DOI: https://doi.org/10.37506/v11%2Fi1%2F2020%2Fijphrd%2F194010